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Tier-1 brand and metro location increase competition, but seniority and niche AI specialization moderate applicant density.
Strong ML/AI and data platform focus limits transferability across non-AI industries.
Explicit 15+ years and specific LLM/AI platform requirements make hiring filters highly restrictive.
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Define and drive technical strategy and architecture for applied AI capabilities and big data infrastructure, ensuring scalable, reliable, and maintainable AI and data systems.
Lead end-to-end lifecycle management of AI applications including design, development, deployment, and integration with enterprise platforms, using Agile methodologies.
Build and mentor high-performing engineering teams while establishing best practices for AI engineering, responsible AI governance, and operational excellence in a regulated environment.
15+ years of experience in building and deploying AI/ML or engineering systems, including recent production experience with LLM-based applications.
3+ years of leadership experience managing teams or large cross-functional initiatives with proven technical delivery impact.
Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, AI/ML, or related quantitative field.
Strong proficiency in Python, AI/ML frameworks (PyTorch, TensorFlow), agent orchestration frameworks (e.g., LangGraph), and cloud-native deployment with container orchestration.
Experienced strategic leader who can translate business priorities into scalable AI solutions within fast-moving, evolving technology environments.
Hands-on architect with deep expertise in multi-agent AI workflows, generative AI, NLP, and enterprise data integration.
Proven capability to establish engineering rigor, responsible AI standards, and reusable platform capabilities rather than isolated prototypes, especially in regulated financial services or asset management domains.